Partitioning a 3D space based on dimensions of the 3D space

ABSTRACT

A method of point cloud geometry decoding including receiving, from a coded bitstream for a point cloud that includes a set of points in a three-dimensional (3D) space, first signaling information being indicative of partition information of the point cloud, and receiving second signaling information that indicates whether the 3D space is an asymmetric cuboid. The method further includes determining dimensions of the 3D space that are signaled along x, y, and z directions based on the second signaling information indicating that the 3D space is the asymmetric cuboid. The method further includes, in response to the first signaling information indicating that the 3D space is the asymmetric cuboid, partitioning the 3D space based on the determined dimensions of the 3D space and based on the partition information indicated by the first signaling information, and reconstructing, by the processing circuitry, the point cloud based on the partitioned 3D space.

INCORPORATION BY REFERENCE

The present application is a continuation of U.S. application Ser. No.17/203,155 filed on Mar. 16, 2021, which claims the benefit of priorityto U.S. Provisional Application No. 63/004,304, “METHOD AND APPARATUSFOR FLEXIBLE QUAD-TREE AND BINARY-TREE PARTITIONING FOR GEOMETRY CODING”filed on Apr. 2, 2020. The disclosures of the prior applications arehereby incorporated by reference in their entirety.

TECHNICAL FIELD

The present disclosure describes embodiments generally related to pointcloud coding.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Various technologies are developed to capture and represent the world,such as objects in the world, environments in the world, and the like in3-dimensional (3D) space. 3D representations of the world can enablemore immersive forms of interaction and communication. Point clouds canbe used as a 3D representation of the world. A point cloud is a set ofpoints in a 3D space, each with associated attributes, e.g. color,material properties, texture information, intensity attributes,reflectivity attributes, motion related attributes, modality attributes,and/or various other attributes. Such point clouds may include largeamounts of data and may be costly and time-consuming to store andtransmit.

SUMMARY

Aspects of the disclosure provide methods and apparatuses for pointcloud compression and decompression. According to an aspect of thedisclosure, a method of point cloud geometry decoding in a point clouddecoder is provided. In the method, first signaling information can bereceived from a coded bitstream for a point cloud that includes a set ofpoints in a three-dimensional (3D) space. The first signalinginformation can indicate partition information of the point cloud.Second signaling information can be determined based on the firstsignaling information indicating a first value. The second signalinginformation can be indicative of a partition mode of the set of pointsin the 3D space. Further, the partition mode of the set of points in the3D space can be determined based on the second signaling information.The point cloud can be reconstructed subsequently based on the partitionmode.

In some embodiments, the partition mode can be determined to be apre-defined Quad-tree and Binary-tree (QtBt) partition based on thesecond signaling information indicating a second value.

In the method, third signaling information can be received thatindicates the 3D space is an asymmetric cuboid. Dimensions of the 3Dspace that are signaled along x, y, and z directions can be determinedbased on the third signaling information indicating the first value.

In some embodiments, 3-bit signaling information can be determined foreach of a plurality of partition levels in the partition mode based onthe second signaling information indicating the first value. The 3-bitsignaling information for each of the plurality of partition levels canbe indicative of partition directions along x, y, and z directions forthe respective partition level in the partition mode.

In some embodiments, the 3-bit signaling information can be determinedbased on dimensions of the 3D space.

In the method, the partition mode can be determined based on the firstsignaling information indicating a second value, where the partitionmode can include a respective octree-partition in each of a plurality ofpartition levels in the partition mode.

According to an aspect of the disclosure, a method of point cloudgeometry decoding in a point cloud decoder is provided. In the method,first signaling information can be received from a coded bitstream for apoint cloud that includes a set of points in a three-dimensional (3D)space. The first signaling information can be indicative of partitioninformation of the point cloud. A partition mode of the set of points inthe 3D space can be determined based on the first signaling information,where the partition mode can include a plurality of partition levels.The point cloud can subsequently be reconstructed based on the partitionmode.

In some embodiments, 3-bit signaling information for each of theplurality of partition levels in the partition mode can be determinedbased on the first signaling information indicating a first value, wherethe 3-bit signaling information for each of the plurality of partitionlevels can be indicative of partition directions along x, y, and zdirections for the respective partition level in the partition mode.

In some embodiments, the 3-bit signaling information can be determinedbased on dimensions of the 3D space.

In some embodiments, the partition mode can be determined to include arespective octree-partition in each of the plurality of partition levelsin the partition mode based on the first signaling informationindicating a second value.

In the method, second signaling information can further be received fromthe coded bitstream for the point cloud. The second signalinginformation can indicate the 3D space is an asymmetric cuboid when thesecond signaling information is a first value, and the 3D space is asymmetric cuboid when the second signaling information is a secondvalue.

In some embodiments, based on the first signal information indicatingthe second value and the second signal information indicating the firstvalue, the partition mode can be determined to include a respectiveoctree-partition in each of first partition levels in the plurality ofpartition levels of the partition mode. A partition type and a partitiondirection of a last partition level of the plurality of partition levelsof the partition mode can be determined according to conditions asfollows:

Partition type Qt along Qt along Qt along and direction x-y axes x-zaxes y-z axes Condition d_(z) = 0 < d_(x) = d_(y) d_(y) = 0 < d_(x) =d_(z) d_(x) = 0 < d_(y) = d_(z) Partition type Bt along Bt along Btalong and direction x axis y axis z axis Condition d_(y) = 0 ≤ d_(z) <d_(x) d_(x) = 0 ≤ d_(z) < d_(y) d_(x) = 0 ≤ d_(y) < d_(z) d_(z) = 0 ≤d_(y) < d_(x) d_(z) = 0 ≤ d_(x) < d_(y) d_(y) = 0 ≤ d_(x) < d_(z),wherein the d_(x), d_(y), and d_(z) are log 2 sizes of the 3D space inthe x, y, and z directions, respectively.

In the method, second signaling information can be determined based onthe first signaling information indicating a first value. The secondsignaling information can indicate the 3D space is an asymmetric cuboidwhen the second signaling information indicates the first value, and the3D space is a symmetric cuboid when the second signaling informationindicates a second value. Further, dimensions of the 3D space that aresignaled along x, y, and z directions can be determined based on thesecond signaling information indicating the first value.

In some examples, the apparatus for processing point cloud data includesreceiving circuitry and processing circuitry that are configured toperform one or more of the methods described above.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, the nature, and various advantages of the disclosedsubject matter will be more apparent from the following detaileddescription and the accompanying drawings in which:

FIG. 1 is a schematic illustration of a simplified block diagram of acommunication system in accordance with an embodiment;

FIG. 2 is a schematic illustration of a simplified block diagram of astreaming system in accordance with an embodiment;

FIG. 3 shows a block diagram of an encoder for encoding point cloudframes, according to some embodiments;

FIG. 4 shows a block diagram of a decoder for decoding a compressedbitstream corresponding to point cloud frames according to someembodiments;

FIG. 5 is a schematic illustration of a simplified block diagram of avideo decoder in accordance with an embodiment;

FIG. 6 is a schematic illustration of a simplified block diagram of avideo encoder in accordance with an embodiment;

FIG. 7 shows a block diagram of a decoder for decoding a compressedbitstream corresponding to point cloud frames according to someembodiments;

FIG. 8 shows a block diagram of an encoder for encoding point cloudframes, according to some embodiments;

FIG. 9 shows a diagram illustrating a partition of a cube based on theoctree partition technique according to some embodiments of thedisclosure.

FIG. 10 shows an example of an octree partition and an octree structurecorresponding to the octree partition according to some embodiments ofthe disclosure.

FIG. 11 shows a point cloud with a shorter bounding box in a z-directionaccording to some embodiments of the disclosure.

FIG. 12 shows a diagram illustrating partitions of a cube based on theoctree partition technique along x-y, x-z, and y-z axes, according tosome embodiments of the disclosure.

FIG. 13 shows a diagram illustrating partitions of a cube based on abinary partition technique along x, y, and z axes, according to someembodiments of the disclosure.

FIG. 14 shows a first flow chart outlining a first process example inaccordance with some embodiments.

FIG. 15 shows a second flow chart outlining a second process example inaccordance with some embodiments.

FIG. 16 is a schematic illustration of a computer system in accordancewith an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

Advanced 3D representations of the world are enabling more immersiveforms of interaction and communication, and also allow machines tounderstand, interpret and navigate our world. 3D point clouds haveemerged as an enabling representation of such information. A number ofapplication cases associated with point cloud data have been identified,and corresponding requirements for point cloud representation andcompression have been developed. For example, a 3D point cloud can beused in autonomous driving for object detection and localization. The 3Dpoint cloud can be also used in geographic information systems (GIS) formapping, and used in cultural heritage to visualize and archive culturalheritage objects and collections.

A point cloud generally may refer to a set of points in a 3D space, eachwith associated attributes. The attributes can include color, materialproperties, texture information, intensity attributes, reflectivityattributes, motion related attributes, modality attributes, and/orvarious other attributes. Point clouds can be used to reconstruct anobject or a scene as a composition of such points. The points can becaptured using multiple cameras, depth sensors and/or Lidar in varioussetups and may be made up of thousands up to billions of points in orderto realistically represent reconstructed scenes.

Compression technologies can reduce the amount of data required torepresent a point cloud for faster transmission or reduction of storage.As such, technologies are needed for lossy compression of point cloudsfor use in real-time communications and six Degrees of Freedom (6 DoF)virtual reality. In addition, technology is sought for lossless pointcloud compression in the context of dynamic mapping for autonomousdriving and cultural heritage applications, and the like. Thus, ISO/IECMPEG (JTC 1/SC 29/WG 11) has started working on a standard to addresscompression of geometry and attributes such as colors and reflectance,scalable/progressive coding, coding of sequences of point cloudscaptured over time, and random access to subsets of the point cloud.

FIG. 1 illustrates a simplified block diagram of a communication system(100) according to an embodiment of the present disclosure. Thecommunication system (100) includes a plurality of terminal devices thatcan communicate with each other, via, for example, a network (150). Forexample, the communication system (100) includes a pair of terminaldevices (110) and (120) interconnected via the network (150). In theFIG. 1 example, the first pair of terminal devices (110) and (120) mayperform unidirectional transmission of point cloud data. For example,the terminal device (110) may compress a point cloud (e.g., pointsrepresenting a structure) that is captured by a sensor (105) connectedwith the terminal device (110). The compressed point cloud can betransmitted, for example in the form of a bitstream, to the otherterminal device (120) via the network (150). The terminal device (120)may receive the compressed point cloud from the network (150),decompress the bitstream to reconstruct the point cloud, and suitablydisplay the reconstructed point cloud. Unidirectional data transmissionmay be common in media serving applications and the like.

In the FIG. 1 example, the terminal devices (110) and (120) may beillustrated as servers, and personal computers, but the principles ofthe present disclosure may be not so limited. Embodiments of the presentdisclosure find application with laptop computers, tablet computers,smart phones, gaming terminals, media players, and/or dedicatedthree-dimensional (3D) equipment. The network (150) represents anynumber of networks that transmit a compressed point cloud between theterminal devices (110) and (120). The network (150) can include forexample wireline (wired) and/or wireless communication networks. Thenetwork (150) may exchange data in circuit-switched and/orpacket-switched channels. Representative networks includetelecommunications networks, local area networks, wide area networks,and/or the Internet. For the purposes of the present discussion, thearchitecture and topology of the network (150) may be immaterial to theoperation of the present disclosure unless explained herein below.

FIG. 2 illustrates a simplified block diagram of a streaming system(200) in accordance with an embodiment. The FIG. 2 example is anapplication for the disclosed subject matter for a point cloud. Thedisclosed subject matter can be equally applicable to other point cloudenabled applications, such as, a 3D telepresence application, virtualreality application, and the like.

The streaming system (200) may include a capture subsystem (213). Thecapture subsystem (213) can include a point cloud source (201), forexample light detection and ranging (LIDAR) systems, 3D cameras, 3Dscanners, a graphics generation component that generates theuncompressed point cloud in software, and the like that generates forexample point clouds (202) that are uncompressed. In an example, thepoint clouds (202) include points that are captured by the 3D cameras.The point clouds (202), depicted as a bold line to emphasize a high datavolume when compared to compressed point clouds (204) (a bitstream ofcompressed point clouds). The compressed point clouds (204) can begenerated by an electronic device (220) that includes an encoder (203)coupled to the point cloud source (201). The encoder (203) can includehardware, software, or a combination thereof to enable or implementaspects of the disclosed subject matter as described in more detailbelow. The compressed point clouds (204) (or bitstream of compressedpoint clouds (204)), depicted as a thin line to emphasize the lower datavolume when compared to the stream of point clouds (202), can be storedon a streaming server (205) for future use. One or more streaming clientsubsystems, such as client subsystems (206) and (208) in FIG. 2 canaccess the streaming server (205) to retrieve copies (207) and (209) ofthe compressed point cloud (204). A client subsystem (206) can include adecoder (210), for example, in an electronic device (230). The decoder(210) decodes the incoming copy (207) of the compressed point clouds andcreates an outgoing stream of reconstructed point clouds (211) that canbe rendered on a rendering device (212).

It is noted that the electronic devices (220) and (230) can includeother components (not shown). For example, the electronic device (220)can include a decoder (not shown) and the electronic device (230) caninclude an encoder (not shown) as well.

In some streaming systems, the compressed point clouds (204), (207), and(209) (e.g., bitstreams of compressed point clouds) can be compressedaccording to certain standards. In some examples, video coding standardsare used in the compression of point clouds. Examples of those standardsinclude, High Efficiency Video Coding (HEVC), Versatile Video Coding(VVC), and the like.

FIG. 3 shows a block diagram of a V-PCC encoder (300) for encoding pointcloud frames, according to some embodiments. In some embodiments, theV-PCC encoder (300) can be used in the communication system (100) andstreaming system (200). For example, the encoder (203) can be configuredand operate in a similar manner as the V-PCC encoder (300).

The V-PCC encoder (300) receives point cloud frames as uncompressedinputs and generates bitstream corresponding to compressed point cloudframes. In some embodiments, the V-PCC encoder (300) may receive thepoint cloud frames from a point cloud source, such as the point cloudsource (201) and the like.

In the FIG. 3 example, the V-PCC encoder (300) includes a patchgeneration module (306), a patch packing module (308), a geometry imagegeneration module (310), a texture image generation module (312), apatch info module (304), an occupancy map module (314), a smoothingmodule (336), image padding modules (316) and (318), a group dilationmodule (320), video compression modules (322), (323) and (332), anauxiliary patch info compression module (338), an entropy compressionmodule (334), and a multiplexer (324).

According to an aspect of the disclosure, the V-PCC encoder (300),converts 3D point cloud frames into an image-based representation alongwith some meta data (e.g., occupancy map and patch info) that is used toconvert the compressed point cloud back into a decompressed point cloud.In some examples, the V-PCC encoder (300) can convert 3D point cloudframes into geometry images, texture images and occupancy maps, and thenuse video coding techniques to encode the geometry images, textureimages and occupancy maps into a bitstream. Generally, a geometry imageis a 2D image with pixels filled with geometry values associated withpoints projected to the pixels, and a pixel filled with a geometry valuecan be referred to as a geometry sample. A texture image is a 2D imagewith pixels filled with texture values associated with points projectedto the pixels, and a pixel filled with a texture value can be referredto as a texture sample. An occupancy map is a 2D image with pixelsfilled with values that indicate occupied or unoccupied by patches.

A patch generally may refer to a contiguous subset of the surfacedescribed by the point cloud. In an example, a patch includes pointswith surface normal vectors that deviate from one another less than athreshold amount. The patch generation module (306) segments a pointcloud into a set of patches, which may be overlapping or not, such thateach patch may be described by a depth field with respect to a plane in2D space. In some embodiments, the patch generation module (306) aims atdecomposing the point cloud into a minimum number of patches with smoothboundaries, while also minimizing the reconstruction error.

The patch info module (304) can collect the patch information thatindicates sizes and shapes of the patches. In some examples, the patchinformation can be packed into an image frame and then encoded by theauxiliary patch info compression module (338) to generate the compressedauxiliary patch information.

The patch packing module (308) is configured to map the extractedpatches onto a 2 dimensional (2D) grid while minimize the unused spaceand guarantee that every M×M (e.g., 16×16) block of the grid isassociated with a unique patch. Efficient patch packing can directlyimpact the compression efficiency either by minimizing the unused spaceor ensuring temporal consistency.

The geometry image generation module (310) can generate 2D geometryimages associated with geometry of the point cloud at given patchlocations. The texture image generation module (312) can generate 2Dtexture images associated with texture of the point cloud at given patchlocations. The geometry image generation module (310) and the textureimage generation module (312) exploit the 3D to 2D mapping computedduring the packing process to store the geometry and texture of thepoint cloud as images. In order to better handle the case of multiplepoints being projected to the same sample, each patch is projected ontotwo images, referred to as layers. In an example, a geometry image isrepresented by a monochromatic frame of W×H in YUV420-8 bit format. Togenerate the texture image, the texture generation procedure exploitsthe reconstructed/smoothed geometry in order to compute the colors to beassociated with the re-sampled points.

The occupancy map module (314) can generate an occupancy map thatdescribes padding information at each unit. For example, the occupancyimage includes a binary map that indicates for each cell of the gridwhether the cell belongs to the empty space or to the point cloud. In anexample, the occupancy map uses binary information describing for eachpixel whether the pixel is padded or not. In another example, theoccupancy map uses binary information describing for each block ofpixels whether the block of pixels is padded or not.

The occupancy map generated by the occupancy map module (314) can becompressed using lossless coding or lossy coding. When lossless codingis used, the entropy compression module (334) is used to compress theoccupancy map. When lossy coding is used, the video compression module(332) is used to compress the occupancy map.

It is noted that the patch packing module (308) may leave some emptyspaces between 2D patches packed in an image frame. The image paddingmodules (316) and (318) can fill the empty spaces (referred to aspadding) in order to generate an image frame that may be suited for 2Dvideo and image codecs. The image padding is also referred to asbackground filling which can fill the unused space with redundantinformation. In some examples, a good background filling minimallyincreases the bit rate and does not introduce significant codingdistortion around the patch boundaries.

The video compression modules (322), (323), and (332) can encode the 2Dimages, such as the padded geometry images, padded texture images, andoccupancy maps based on a suitable video coding standard, such as HEVC,VVC and the like. In an example, the video compression modules (322),(323), and (332) are individual components that operate separately. Itis noted that the video compression modules (322), (323), and (332) canbe implemented as a single component in another example.

In some examples, the smoothing module (336) is configured to generate asmoothed image of the reconstructed geometry image. The smoothed imagecan be provided to the texture image generation (312). Then, the textureimage generation (312) may adjust the generation of the texture imagebased on the reconstructed geometry images. For example, when a patchshape (e.g., geometry) is slightly distorted during encoding anddecoding, the distortion may be taken into account when generating thetexture images to correct for the distortion in patch shape.

In some embodiments, the group dilation (320) is configured to padpixels around the object boundaries with redundant low-frequency contentin order to improve coding gain as well as visual quality ofreconstructed point cloud.

The multiplexer (324) can multiplex the compressed geometry image, thecompressed texture image, the compressed occupancy map, and/or thecompressed auxiliary patch information into a compressed bitstream.

FIG. 4 shows a block diagram of a V-PCC decoder (400) for decoding acompressed bitstream corresponding to point cloud frames, according tosome embodiments. In some embodiments, the V-PCC decoder (400) can beused in the communication system (100) and streaming system (200). Forexample, the decoder (210) can be configured to operate in a similarmanner as the V-PCC decoder (400). The V-PCC decoder (400) receives thecompressed bitstream, and generates a reconstructed point cloud based onthe compressed bitstream.

In the FIG. 4 example, the V-PCC decoder (400) includes a de-multiplexer(432), video decompression modules (434) and (436), an occupancy mapdecompression module (438), an auxiliary patch-information decompressionmodule (442), a geometry reconstruction module (444), a smoothing module(446), a texture reconstruction module (448), and a color smoothingmodule (452).

The de-multiplexer (432) can receive and separate the compressedbitstream into a compressed texture image, compressed geometry image,compressed occupancy map, and compressed auxiliary patch information.

The video decompression modules (434) and (436) can decode thecompressed images according to a suitable standard (e.g., HEVC, VVC,etc.) and output decompressed images. For example, the videodecompression module (434) decodes the compressed texture images andoutputs decompressed texture images; and the video decompression module(436) decodes the compressed geometry images and outputs thedecompressed geometry images.

The occupancy map decompression module (438) can decode the compressedoccupancy maps according to a suitable standard (e.g., HEVC, VVC, etc.)and output decompressed occupancy maps.

The auxiliary patch-information decompression module (442) can decodethe compressed auxiliary patch information according to a suitablestandard (e.g., HEVC, VVC, etc.) and output decompressed auxiliary patchinformation.

The geometry reconstruction module (444) can receive the decompressedgeometry images, and generate reconstructed point cloud geometry basedon the decompressed occupancy map and decompressed auxiliary patchinformation.

The smoothing module (446) can smooth incongruences at edges of patches.The smoothing procedure aims at alleviating potential discontinuitiesthat may arise at the patch boundaries due to compression artifacts. Insome embodiments, a smoothing filter may be applied to the pixelslocated on the patch boundaries to alleviate the distortions that may becaused by the compression/decompression.

The texture reconstruction module (448) can determine textureinformation for points in the point cloud based on the decompressedtexture images and the smoothing geometry.

The color smoothing module (452) can smooth incongruences of coloring.Non-neighboring patches in 3D space are often packed next to each otherin 2D videos. In some examples, pixel values from non-neighboringpatches might be mixed up by the block-based video codec. The goal ofcolor smoothing is to reduce the visible artifacts that appear at patchboundaries.

FIG. 5 shows a block diagram of a video decoder (510) according to anembodiment of the present disclosure. The video decoder (510) can beused in the V-PCC decoder (400). For example, the video decompressionmodules (434) and (436), the occupancy map decompression module (438)can be similarly configured as the video decoder (510).

The video decoder (510) may include a parser (520) to reconstructsymbols (521) from compressed images, such as the coded video sequence.Categories of those symbols include information used to manage operationof the video decoder (510). The parser (520) may parse/entropy-decodethe coded video sequence that is received. The coding of the coded videosequence can be in accordance with a video coding technology orstandard, and can follow various principles, including variable lengthcoding, Huffman coding, arithmetic coding with or without contextsensitivity, and so forth. The parser (520) may extract from the codedvideo sequence, a set of subgroup parameters for at least one of thesubgroups of pixels in the video decoder, based upon at least oneparameter corresponding to the group. Subgroups can include Groups ofPictures (GOPs), pictures, tiles, slices, macroblocks, Coding Units(CUs), blocks, Transform Units (TUs), Prediction Units (PUs) and soforth. The parser (520) may also extract from the coded video sequenceinformation such as transform coefficients, quantizer parameter values,motion vectors, and so forth.

The parser (520) may perform an entropy decoding/parsing operation onthe video sequence received from a buffer memory, so as to createsymbols (521).

Reconstruction of the symbols (521) can involve multiple different unitsdepending on the type of the coded video picture or parts thereof (e.g.,inter and intra picture, inter and intra block), and other factors.Which units are involved, and how, can be controlled by the subgroupcontrol information that was parsed from the coded video sequence by theparser (520). The flow of such subgroup control information between theparser (520) and the multiple units below is not depicted for clarity.

Beyond the functional blocks already mentioned, the video decoder (510)can be conceptually subdivided into a number of functional units asdescribed below. In a practical implementation operating undercommercial constraints, many of these units interact closely with eachother and can, at least partly, be integrated into each other. However,for the purpose of describing the disclosed subject matter, theconceptual subdivision into the functional units below is appropriate.

A first unit is the scaler/inverse transform unit (551). Thescaler/inverse transform unit (551) receives a quantized transformcoefficient as well as control information, including which transform touse, block size, quantization factor, quantization scaling matrices,etc. as symbol(s) (521) from the parser (520). The scaler/inversetransform unit (551) can output blocks comprising sample values that canbe input into aggregator (555).

In some cases, the output samples of the scaler/inverse transform (551)can pertain to an intra coded block; that is: a block that is not usingpredictive information from previously reconstructed pictures, but canuse predictive information from previously reconstructed parts of thecurrent picture. Such predictive information can be provided by an intrapicture prediction unit (552). In some cases, the intra pictureprediction unit (552) generates a block of the same size and shape ofthe block under reconstruction, using surrounding already reconstructedinformation fetched from the current picture buffer (558). The currentpicture buffer (558) buffers, for example, a partly reconstructedcurrent picture and/or fully reconstructed current picture. Theaggregator (555), in some cases, adds, on a per sample basis, theprediction information the intra prediction unit (552) has generated tothe output sample information as provided by the scaler/inversetransform unit (551).

In other cases, the output samples of the scaler/inverse transform unit(551) can pertain to an inter coded, and potentially motion compensatedblock. In such a case, a motion compensation prediction unit (553) canaccess reference picture memory (557) to fetch samples used forprediction. After motion compensating the fetched samples in accordancewith the symbols (521) pertaining to the block, these samples can beadded by the aggregator (555) to the output of the scaler/inversetransform unit (551) (in this case called the residual samples orresidual signal) so as to generate output sample information. Theaddresses within the reference picture memory (557) from where themotion compensation prediction unit (553) fetches prediction samples canbe controlled by motion vectors, available to the motion compensationprediction unit (553) in the form of symbols (521) that can have, forexample X, Y, and reference picture components. Motion compensation alsocan include interpolation of sample values as fetched from the referencepicture memory (557) when sub-sample exact motion vectors are in use,motion vector prediction mechanisms, and so forth.

The output samples of the aggregator (555) can be subject to variousloop filtering techniques in the loop filter unit (556). Videocompression technologies can include in-loop filter technologies thatare controlled by parameters included in the coded video sequence (alsoreferred to as coded video bitstream) and made available to the loopfilter unit (556) as symbols (521) from the parser (520), but can alsobe responsive to meta-information obtained during the decoding ofprevious (in decoding order) parts of the coded picture or coded videosequence, as well as responsive to previously reconstructed andloop-filtered sample values.

The output of the loop filter unit (556) can be a sample stream that canbe output to a render device as well as stored in the reference picturememory (557) for use in future inter-picture prediction.

Certain coded pictures, once fully reconstructed, can be used asreference pictures for future prediction. For example, once a codedpicture corresponding to a current picture is fully reconstructed andthe coded picture has been identified as a reference picture (by, forexample, the parser (520)), the current picture buffer (558) can becomea part of the reference picture memory (557), and a fresh currentpicture buffer can be reallocated before commencing the reconstructionof the following coded picture.

The video decoder (510) may perform decoding operations according to apredetermined video compression technology in a standard, such as ITU-TRec. H.265. The coded video sequence may conform to a syntax specifiedby the video compression technology or standard being used, in the sensethat the coded video sequence adheres to both the syntax of the videocompression technology or standard and the profiles as documented in thevideo compression technology or standard. Specifically, a profile canselect certain tools as the only tools available for use under thatprofile from all the tools available in the video compression technologyor standard. Also necessary for compliance can be that the complexity ofthe coded video sequence is within bounds as defined by the level of thevideo compression technology or standard. In some cases, levels restrictthe maximum picture size, maximum frame rate, maximum reconstructionsample rate (measured in, for example megasamples per second), maximumreference picture size, and so on. Limits set by levels can, in somecases, be further restricted through Hypothetical Reference Decoder(HRD) specifications and metadata for HRD buffer management signaled inthe coded video sequence.

FIG. 6 shows a block diagram of a video encoder (603) according to anembodiment of the present disclosure. The video encoder (603) can beused in the V-PCC encoder (300) that compresses point clouds. In anexample, the video compression module (322) and (323), and the videocompression module (332) are configured similarly to the encoder (603).

The video encoder (603) may receive images, such as padded geometryimages, padded texture images and the like, and generate compressedimages.

According to an embodiment, the video encoder (603) may code andcompress the pictures of the source video sequence (images) into a codedvideo sequence (compressed images) in real time or under any other timeconstraints as required by the application. Enforcing appropriate codingspeed is one function of a controller (650). In some embodiments, thecontroller (650) controls other functional units as described below andis functionally coupled to the other functional units. The coupling isnot depicted for clarity. Parameters set by the controller (650) caninclude rate control related parameters (picture skip, quantizer, lambdavalue of rate-distortion optimization techniques, . . . ), picture size,group of pictures (GOP) layout, maximum motion vector search range, andso forth. The controller (650) can be configured to have other suitablefunctions that pertain to the video encoder (603) optimized for acertain system design.

In some embodiments, the video encoder (603) is configured to operate ina coding loop. As an oversimplified description, in an example, thecoding loop can include a source coder (630) (e.g., responsible forcreating symbols, such as a symbol stream, based on an input picture tobe coded, and a reference picture(s)), and a (local) decoder (633)embedded in the video encoder (603). The decoder (633) reconstructs thesymbols to create the sample data in a similar manner as a (remote)decoder also would create (as any compression between symbols and codedvideo bitstream is lossless in the video compression technologiesconsidered in the disclosed subject matter). The reconstructed samplestream (sample data) is input to the reference picture memory (634). Asthe decoding of a symbol stream leads to bit-exact results independentof decoder location (local or remote), the content in the referencepicture memory (634) is also bit exact between the local encoder andremote encoder. In other words, the prediction part of an encoder “sees”as reference picture samples exactly the same sample values as a decoderwould “see” when using prediction during decoding. This fundamentalprinciple of reference picture synchronicity (and resulting drift, ifsynchronicity cannot be maintained, for example because of channelerrors) is used in some related arts as well.

The operation of the “local” decoder (633) can be the same as of a“remote” decoder, such as the video decoder (510), which has alreadybeen described in detail above in conjunction with FIG. 5 . Brieflyreferring also to FIG. 5 , however, as symbols are available andencoding/decoding of symbols to a coded video sequence by an entropycoder (645) and the parser (520) can be lossless, the entropy decodingparts of the video decoder (510), including and parser (520) may not befully implemented in the local decoder (633).

An observation that can be made at this point is that any decodertechnology except the parsing/entropy decoding that is present in adecoder also necessarily needs to be present, in substantially identicalfunctional form, in a corresponding encoder. For this reason, thedisclosed subject matter focuses on decoder operation. The descriptionof encoder technologies can be abbreviated as they are the inverse ofthe comprehensively described decoder technologies. Only in certainareas a more detail description is required and provided below.

During operation, in some examples, the source coder (630) may performmotion compensated predictive coding, which codes an input picturepredictively with reference to one or more previously-coded picture fromthe video sequence that were designated as “reference pictures.” In thismanner, the coding engine (632) codes differences between pixel blocksof an input picture and pixel blocks of reference picture(s) that may beselected as prediction reference(s) to the input picture.

The local video decoder (633) may decode coded video data of picturesthat may be designated as reference pictures, based on symbols createdby the source coder (630). Operations of the coding engine (632) mayadvantageously be lossy processes. When the coded video data may bedecoded at a video decoder (not shown in FIG. 6 ), the reconstructedvideo sequence typically may be a replica of the source video sequencewith some errors. The local video decoder (633) replicates decodingprocesses that may be performed by the video decoder on referencepictures and may cause reconstructed reference pictures to be stored inthe reference picture cache (634). In this manner, the video encoder(603) may store copies of reconstructed reference pictures locally thathave common content as the reconstructed reference pictures that will beobtained by a far-end video decoder (absent transmission errors).

The predictor (635) may perform prediction searches for the codingengine (632). That is, for a new picture to be coded, the predictor(635) may search the reference picture memory (634) for sample data (ascandidate reference pixel blocks) or certain metadata such as referencepicture motion vectors, block shapes, and so on, that may serve as anappropriate prediction reference for the new pictures. The predictor(635) may operate on a sample block-by-pixel block basis to findappropriate prediction references. In some cases, as determined bysearch results obtained by the predictor (635), an input picture mayhave prediction references drawn from multiple reference pictures storedin the reference picture memory (634).

The controller (650) may manage coding operations of the source coder(630), including, for example, setting of parameters and subgroupparameters used for encoding the video data.

Output of all aforementioned functional units may be subjected toentropy coding in the entropy coder (645). The entropy coder (645)translates the symbols as generated by the various functional units intoa coded video sequence, by lossless compressing the symbols to generatecompressed images 643 according to technologies such as Huffman coding,variable length coding, arithmetic coding, and so forth.

The controller (650) may manage operation of the video encoder (603).During coding, the controller (650) may assign to each coded picture acertain coded picture type, which may affect the coding techniques thatmay be applied to the respective picture. For example, pictures oftenmay be assigned as one of the following picture types:

An Intra Picture (I picture) may be one that may be coded and decodedwithout using any other picture in the sequence as a source ofprediction. Some video codecs allow for different types of intrapictures, including, for example Independent Decoder Refresh (“IDR”)Pictures. A person skilled in the art is aware of those variants of Ipictures and their respective applications and features.

A predictive picture (P picture) may be one that may be coded anddecoded using intra prediction or inter prediction using at most onemotion vector and reference index to predict the sample values of eachblock.

A bi-directionally predictive picture (B Picture) may be one that may becoded and decoded using intra prediction or inter prediction using atmost two motion vectors and reference indices to predict the samplevalues of each block. Similarly, multiple-predictive pictures can usemore than two reference pictures and associated metadata for thereconstruction of a single block.

Source pictures commonly may be subdivided spatially into a plurality ofsample blocks (e.g., blocks of 4×4, 8×8, 4×8, or 16×16 samples each) andcoded on a block-by-block basis. Blocks may be coded predictively withreference to other (already coded) blocks as determined by the codingassignment applied to the blocks' respective pictures. For example,blocks of I pictures may be coded non-predictively or they may be codedpredictively with reference to already coded blocks of the same picture(spatial prediction or intra prediction). Pixel blocks of P pictures maybe coded predictively, via spatial prediction or via temporal predictionwith reference to one previously coded reference picture. Blocks of Bpictures may be coded predictively, via spatial prediction or viatemporal prediction with reference to one or two previously codedreference pictures.

The video encoder (603) may perform coding operations according to apredetermined video coding technology or standard, such as ITU-T Rec.H.265. In its operation, the video encoder (603) may perform variouscompression operations, including predictive coding operations thatexploit temporal and spatial redundancies in the input video sequence.The coded video data, therefore, may conform to a syntax specified bythe video coding technology or standard being used.

A video may be in the form of a plurality of source pictures (images) ina temporal sequence. Intra-picture prediction (often abbreviated tointra prediction) makes use of spatial correlation in a given picture,and inter-picture prediction makes uses of the (temporal or other)correlation between the pictures. In an example, a specific pictureunder encoding/decoding, which is referred to as a current picture, ispartitioned into blocks. When a block in the current picture is similarto a reference block in a previously coded and still buffered referencepicture in the video, the block in the current picture can be coded by avector that is referred to as a motion vector. The motion vector pointsto the reference block in the reference picture, and can have a thirddimension identifying the reference picture, in case multiple referencepictures are in use.

In some embodiments, a bi-prediction technique can be used in theinter-picture prediction. According to the bi-prediction technique, tworeference pictures, such as a first reference picture and a secondreference picture that are both prior in decoding order to the currentpicture in the video (but may be in the past and future, respectively,in display order) are used. A block in the current picture can be codedby a first motion vector that points to a first reference block in thefirst reference picture, and a second motion vector that points to asecond reference block in the second reference picture. The block can bepredicted by a combination of the first reference block and the secondreference block.

Further, a merge mode technique can be used in the inter-pictureprediction to improve coding efficiency.

According to some embodiments of the disclosure, predictions, such asinter-picture predictions and intra-picture predictions are performed inthe unit of blocks. For example, according to the HEVC standard, apicture in a sequence of video pictures is partitioned into coding treeunits (CTU) for compression, the CTUs in a picture have the same size,such as 64×64 pixels, 32×32 pixels, or 16×16 pixels. In general, a CTUincludes three coding tree blocks (CTBs), which are one luma CTB and twochroma CTBs. Each CTU can be recursively quadtree split into one ormultiple coding units (CUs). For example, a CTU of 64×64 pixels can besplit into one CU of 64×64 pixels, or 4 CUs of 32×32 pixels, or 16 CUsof 16×16 pixels. In an example, each CU is analyzed to determine aprediction type for the CU, such as an inter prediction type or an intraprediction type. The CU is split into one or more prediction units (PUs)depending on the temporal and/or spatial predictability. Generally, eachPU includes a luma prediction block (PB), and two chroma PBs. In anembodiment, a prediction operation in coding (encoding/decoding) isperformed in the unit of a prediction block. Using a luma predictionblock as an example of a prediction block, the prediction block includesa matrix of values (e.g., luma values) for pixels, such as 8×8 pixels,16×16 pixels, 8×16 pixels, 16×8 pixels, and the like.

The G-PCC model can separately compress the geometry information and theassociated attributes such as color or reflectance. The geometryinformation, which is the 3D coordinates of the point clouds, can becoded by octree-decomposition of its occupancy information. On the otherhand, the attributes can be compressed based on reconstructed geometryusing prediction and lifting techniques. The octree-partition process isdiscussed in FIGS. 7-13 for example.

FIG. 7 shows a block diagram of a G-PCC decoder (800) that is appliedduring a G-PCC decomposition process in accordance with an embodiment.The decoder (800) can be configured to receive a compressed bitstreamand perform point cloud data decompression to decompress the bitstreamto generate decoded point cloud data. In an embodiment, the decoder(800) can include an arithmetic decoding module (810), an inversequantization module (820), an octree decoding module (830), an LODgeneration module (840), an inverse quantization module (850), and aninverse interpolation-based prediction module (860).

As shown, a compressed bitstream (801) can be received at the arithmeticdecoding module (810). The arithmetic decoding module (810) isconfigured to decode the compressed bitstream (801) to obtain quantizedprediction residuals (if generated) and occupancy codes (or symbols) ofa point cloud. The octree decoding module (830) is configured togenerate quantized positions of points in the point cloud according tothe occupancy codes. The inverse quantization module (850) is configuredto generate reconstructed positions of the points in the point cloudbased on the quantized positions provided by the octree decoding module(830).

The LOD generation module (840) is configured to re-organize the pointsinto different LODs based on the reconstructed positions, and determinean LOD-based order. The inverse quantization module (820) is configuredto generate reconstructed prediction residuals based on the quantizedprediction residuals received from the arithmetic decoding module (810).The inverse interpolation-based prediction module (860) is configured toperform an attribute prediction process to generate reconstructedattributes of the points in the point cloud based on the reconstructedprediction residuals received from the inverse quantization module (820)and the LOD-based order received from the LOD generation module (840).

Further, the reconstructed attributes generated from the inverseinterpolation-based prediction module (860) together with thereconstructed positions generated from the inverse quantization module(850) correspond to a decoded point cloud (or reconstructed point cloud)(802) that is output from the decoder (800) in one example.

FIG. 8 shows a block diagram of a G-PPC encoder (700) in accordance withan embodiment. The encoder (700) can be configured to receive pointcloud data and compress the point cloud data to generate a bit streamcarrying compressed point cloud data. In an embodiment, the encoder(700) can include a position quantization module (710), a duplicatedpoints removal module (712), an octree encoding module (730), anattribute transfer module (720), a level of detail (LOD) generationmodule (740), an interpolation-based prediction module (750), a residualquantization module (760), and an arithmetic coding module (770).

As shown, an input point cloud (701) can be received at the encoder(700). Positions (e.g., 3D coordinates) of the point cloud (701) areprovided to the quantization module (710). The quantization module (710)is configured to quantize the coordinates to generate quantizedpositions. The duplicated points removal module (712) is configured toreceive the quantized positions and perform a filter process to identifyand remove duplicated points. The octree encoding module (730) isconfigured to receive filtered positions from the duplicated pointsremoval module (712), and perform an octree-based encoding process togenerate a sequence of occupancy codes (or symbols) that describe a 3Dgrid of voxels. The occupancy codes are provided to the arithmeticcoding module (770).

The attribute transfer module (720) is configured to receive attributesof the input point cloud, and perform an attribute transfer process todetermine an attribute value for each voxel when multiple attributevalues are associated with the respective voxel. The attribute transferprocess can be performed on the re-ordered points output from the octreeencoding module (730). The attributes after the transfer operations areprovided to the interpolation-based prediction module (750). The LODgeneration module (740) is configured to operate on the re-orderedpoints output from the octree encoding module (730), and re-organize thepoints into different LODs. LOD information is supplied to theinterpolation-based prediction module (750).

The interpolation-based prediction module (750) processes the pointsaccording to an LOD-based order indicated by the LOD information fromthe LOD generation module (740) and the transferred attributes receivedfrom the attribute transfer module (720), and generates predictionresiduals. The residual quantization module (760) is configured toreceive the prediction residuals from the interpolation-based predictionmodule (750), and perform quantization to generate quantized predictionresiduals. The quantized prediction residuals are provided to thearithmetic coding module (770). The arithmetic coding module (770) isconfigured to receive the occupancy codes from the octree encodingmodule (730), the candidate indices (if used), the quantized predictionresiduals from the interpolation-based prediction module (750), andother information, and perform entropy encoding to further compress thereceived values or information. As a result, a compressed bitstream(702) carrying the compressed information can be generated. Thebitstream (702) may be transmitted, or otherwise provided, to a decoderthat decodes the compressed bitstream, or may be stored in a storagedevice.

It is noted that the interpolation-based prediction module (750) and theinverse interpolation-based prediction module (860) configured toimplement the attribute prediction techniques disclosed herein can beincluded in other decoders or encoders that may have similar ordifferent structures from what is shown in FIG. 7 and FIG. 8 . Inaddition, the encoder (700) and decoder (800) can be included in a samedevice, or separate devices in various examples.

In various embodiments, the encoder (300), the decoder (400), theencoder (700), and/or the decoder (800) can be implemented withhardware, software, or combination thereof. For example, the encoder(300), the decoder (400), the encoder (700), and/or the decoder (800)can be implemented with processing circuitry such as one or moreintegrated circuits (ICs) that operate with or without software, such asan application specific integrated circuit (ASIC), field programmablegate array (FPGA), and the like. In another example, the encoder (300),the decoder (400), the encoder (700), and/or the decoder (800) can beimplemented as software or firmware including instructions stored in anon-volatile (or non-transitory) computer-readable storage medium. Theinstructions, when executed by processing circuitry, such as one or moreprocessors, causing the processing circuitry to perform functions of theencoder (300), the decoder (400), the encoder (700), and/or the decoder(800).

Partitioning of a point cloud that is defined by a 3D cube in asymmetric manner along all axes (e.g., x, y and z axis) can result ineight sub-cubes, which is known as an octree (OT) partition in pointcloud compression (PCC). The OT partition resembles a binary-tree (BT)partition in one-dimensional and a quadtree (QT) partition in atwo-dimensional space. The idea of OT partition can be illustrated inFIG. 9 , where a 3D cube (900) in solid can be partitioned into eightsmaller equal-sized cubes in dashed lines. As shown in FIG. 9 , theoctree partition technique can divide the 3D cube (900) into eightsmaller equal-sized cubes 0-7.

In the octree partition technique (e.g., in TMC13), if the Octreegeometry codec is used, the geometry encoding proceeds as follows.First, a cubical axis-aligned bounding box B can be defined by twoextreme points (0,0,0) and (2^(d), 2^(d), 2^(d)), where 2^(d) defines asize of the bounding box B and d can be encoded to a bitstream.Accordingly, all the points inside the defined bounding box B can becompressed.

An octree structure can then be built by recursively subdividing thebounding box B. At each stage, a cube can be subdivided into 8sub-cubes. A size of a sub-cube after being iteratively subdividing k(k≤d) times can be (2^(d−k), 2^(d−k), 2^(d−k)). An 8-bit code, such asan occupancy code, can then be generated by associating a 1-bit valuewith each sub-cube in order to indicate whether the correspondingsub-cube contains points (i.e., full and has value 1) or not (i.e.,empty and has value 0). Only full sub-cubes with a size greater than 1(i.e., non-voxels) can further be subdivided. The occupancy code foreach cube can then be compressed by an arithmetic encoder.

The decoding process can start by reading from the bitstream dimensionsof the bounding box B. A same octree structure can then be built bysubdividing the bounding box B according to the decoded occupancy codes.An example of two-level OT partition and the corresponding occupancycode can be shown in FIG. 10 , where cubes and nodes that are shadedindicate the cubes and nodes are occupied by points.

FIG. 10 shows an example of an octree partition (1010) and an octreestructure (1020) corresponding to the octree partition (1010) accordingto some embodiments of the disclosure. FIG. 10 shows two levels ofpartitions in the octree partition (1010). The octree structure (1020)includes a node (NO) corresponding to the cubical box for octreepartition (1010). At a first level, the cubical box is partitioned into8 sub cubical boxes that are numbered 0-7 according to the numberingtechnique shown in FIG. 9 . The occupancy code for the partition of thenode N0 is “10000001” in binary, which indicates the first sub cubicalbox represented by node N0-0 and the eighth sub cubical box representedby node N0-7 includes points in the point cloud and other sub cubicalboxes are empty.

Then, at the second level of partition, the first sub cubical box(represented by node N0-0) and the eighth sub cubical box (representedby node N0-7) are further respectively sub-divided into eight octants.For example, the first sub cubical box (represented by node N0-0) ispartitioned into 8 smaller sub cubical boxes that are numbered 0-7according to the numbering technique shown in FIG. 9 . The occupancycode for the partition of the node N0-0 is “00011000” in binary, whichindicates the fourth smaller sub cubical box (represented by nodeN0-0-3) and the fifth smaller sub cubical box (represented by nodeN0-0-4) includes points in the point cloud and other smaller sub cubicalboxes are empty. At the second level, the seventh sub cubical box(represented by node N0-7) is similarly partitioned into 8 smaller subcubical boxes as shown in FIG. 10 .

In the FIG. 10 example, the nodes corresponding to non-empty cubicalspace (e.g., cubical box, sub cubical boxes, smaller sub cubical boxesand the like) are shaded in gray, and referred to as shaded nodes.

In the original TMC13 design, for example as described above, thebounding box B may be restricted to be a cube that has a same size forall dimensions, and the OT partition thus can be performed for allsub-cubes at each node at which the sub-cubes are halved in size for alldimensions. The OT partition can be performed recursively until the sizeof sub-cubes reaches one. However, the partition in such a manner mightnot be efficient for all cases, especially when the points arenon-uniformly distributed in a 3D scene (or 3D space).

One extreme case could be a 2D plane in a 3D space, in which all thepoints can be located on a x-y plane in the 3D space and the variationin the z-axis can be zero. In such a case, OT partition performed on acubic B as a starting point could waste extensive bits to representoccupancy information in the z-direction, which is redundant and notuseful. In real applications, the worst case may not occur often.However, it is typical to have a point cloud that has less variance inone direction compared to others. As shown in FIG. 11 , a point cloudsequence named “ford_01_vox1mm” in TMC13 can have principle componentsin x and y directions. In fact, many point cloud data generated from aLidar system can have the same characteristics.

In an quad-tree and binary-tree (QtBt) partitions, the bounding box Bmay not be restricted to be a cube, instead the bounding box B can be anarbitrary-sized rectangular cuboid to better fit for the shape of the 3Dscene or objects. In the implementation, the size of the bounding box Bcan be represented as a power of two, e.g., (2^(d) ^(x) , 2^(d) ^(y) ,2^(d) ^(z) ⁾.

As bounding box B may not be a perfect cube, in some cases the node maynot be (or unable to be) partitioned along all directions. If apartition is performed on all three directions, the partition is atypical OT partition. If the partition is performed on two directionsout of three, the partition is thus a QT partition in 3D. If thepartition is performed on one direction only, the partition is then a BTpartition in 3D. Examples of QT and BT in 3D are shown in FIG. 12 andFIG. 13 , respectively.

As shown in FIG. 12 , a 3D cube 1201 can be partitioned along x-y axesinto 4 sub-cubes 0, 2, 4, and 6. A 3D cube 1202 can be partitioned alongx-z axes into 4 sub-cubes 0, 1, 4 and 5. A 3D cube 1203 can bepartitioned along y-z axes into 4 sub-cubes 0, 1, 2 and 3. In FIG. 13 ,a 3D cube 1301 can be partitioned along an x axis into 2 sub-cubes 0 and4. A 3D cube 1302 can be partitioned into 2 sub-cubes 0 and 2. A 3D cube1303 can be partitioned into 2 sub-cubes 0 and 1.

In order to define conditions of implicit QT and BT partitions in TMC13,two parameters (i.e., K and M) can be applied. The first parameter K(0≤K≤max(d_(x), d_(y), d_(z))−min (d_(x), d_(y), d_(z))) can definemaximum times of implicit QT and BT partitions that can be performedbefore OT partitions. The second parameter M (0≤M≤min (d_(x), d_(y),d_(z))) can define a minimal size of implicit QT and BT partitions,indicating that implicit QT and BT partitions are allowed only if alldimensions are greater than M.

More specifically, the first K partitions can follow rules in Table I,and partitions subsequent to the first K partitions can follow rules inTable II. If none of the conditions listed in tables are met, an OTpartition can be performed.

TABLE I Conditions to perform implicit QT or BT partition for the firstK partitions. QT along QT along QT along x-y axes x-z axes y-z axesCondition d_(z) < d_(x) = d_(y) d_(y) < d_(x) = d_(z) d_(x) < d_(y) =d_(z) BT along BT along BT along x axis y axis z axis Condition d_(y) <d_(x) and d_(z) < d_(x) d_(x) < d_(y) and d_(z) < d_(y) d_(x) < d_(z)and d_(y) < d_(z)

TABLE II Conditions to perform implicit QT or BT partition after thefirst K partitions. QT along QT along QT along x-y axes x-z axes y-zaxes Condition d_(z) = M < d_(x) = d_(y) d_(y) = M < d_(x) = d_(z) d_(x)= M < d_(y) = d_(z) BT along BT along BT along x axis y axis z axisCondition d_(y) = M ≤ d_(z) < d_(x) d_(x) = M ≤ d_(z) < d_(y) d_(x) = M≤ d_(y) < d_(z) d_(z) = M ≤ d_(y) < d_(x) d_(z) = M ≤ d_(x) < d_(y)d_(y) = M ≤ d_(x) < d_(z)

In an embodiment, the bounding box B can have a size of (2^(d) ^(x) ,2^(d) ^(y) , 2^(d) ^(z) ). Without loss of generality, conditions0<d_(x)≤d_(y)≤d_(z) can be applied to the bounding box B. Based on theconditions, at first K (K≤d_(z)−d_(x)) depths, implicit BT partitionscan be performed along a z axis and implicit QT partitions can then beperformed along y-z axes according to Table I. The size of sub-nodesthen can become 2^((d) ^(x) ^(,d) ^(x) ^(+δ) ^(y) ^(,d) ^(x) ^(+d) ^(z)⁾, where the value of δ_(y) and δ_(z) (δ_(z)≥δ_(y)≥0) can depend on thevalue of K. Further, OT partitions can be performed d_(x)−M times sothat the remaining sub-nodes can have a size of 2^((M,M+δ) ^(y) ^(,M+δ)^(z) ⁾. Next, according to Table II, implicit BT partitions can beperformed along the z-axis δ_(z)−δ_(y) times, and implicit QT partitionscan then be performed along y-z axes δ_(y) times. The rest of the nodesthus can have a size of 2^((M,M,M)). Therefore, OT partitions can beperformed M times to reach the smallest units.

In QtBt partitions, an implicit rule is provided on how to applypartitioning of a given cuboid by switching among Octree, Quadtree, andBinary-tree at each level of node decomposition. After the K levels ofinitial decomposition via QtBt partitions according to a rule (e.g.,Table I), another round of QtBt partition can be performed according toanother rule (e.g., Table II). If none of conditions in the rules is metin the above processes, Octree decomposition (or octree partition) canbe applied.

The implicit rule can impact the effectiveness of QtBt as follows: (1)for point cloud data with an almost symmetric cuboid bounding box alongx, y and z dimensions, QtBt partition has not shown coding gains overthe related method (e.g., the implicit QtBt partition), which isperforming Ot (Octree) decomposition at all levels; and (2) for pointcloud data with a highly asymmetric cuboid bounding box along x, y and zdimensions, QtBt partition has shown coding gains by skipping sending ofunnecessary occupancy information during the decomposition.

In the current QtBt partitions, certain restrictions can be placed asfollows. First, the QtBt partitions can always enforce the use ofasymmetric bounding box, which may not be useful or evencounter-productive when the point cloud has an almost symmetric boundingbox. Second, Table I together with the parameter K can reduce the largerdimensions according to the rule by enforcing Qt/Bt partitions insteadof Ot partition. However, Table I together with the parameter K may notallow a Qt or Bt partition at the beginning when the bounding box issymmetric. Third, Table II can be applied after the above K times ofsplit and kicks in when the minimum dimension of sub-boxes reaches M.Thus, Table II can reduce the larger dimensions according to the ruleuntil all dimensions become equal to M. Fourth, the current implicitrule (or implicit QtBt partitions) can always mandate octreedecomposition after the first (up to) K levels until the current QtBtpartitions reach the level M. In other words, the current QtBtpartitions may not allow arbitrary Qt/Bt/Ot partitions to be chosen inbetween the two level-points.

In the present disclosure, multiple methods are provided. The methodsprovide for simplification of the QtBt design (e.g., the implicit QtBtpartitions) in TMC 13 for typical use case, for examples based upon thediscussion above. The methods also allow for more flexible ways ofpartitioning, for example by explicitly signaling the node decompositiontype at each level.

In an embodiment, a first partition method (or simplified QtBtpartition) can be provided. The first partition method can be a specialcase of the implicit QtBt partitions, which can be applied to data setswith highly asymmetric bounding boxes by setting K=0 & M=0. The firstpartition method can simplify the QtBt design (e.g., QtBt partitions)and still bring about coding benefits for typical cases as noted above.

Comparing to the QtBt partitions in TMC 13, the first partition methodcan include the following features: (1) an implicit enabled flag (e.g.,implicit_qtbt_enabled_flag) in the QtBt partitions in TMC 13 can beremoved. (2) an asymmetric bounding box flag (e.g.,asymmetric_bbox_enabled_flag) can be introduced to enable the use of anasymmetric bounding box. In an example, when the asymmetric bounding boxflag is set to a value such as 0 (also referred to a second value) forsymmetric or almost symmetric bounding-box data and to a value such as 1(also referred to as a first value) for highly asymmetric bounding-boxdata. (3) If the asymmetric bounding box flag is the first value, theimplicitQtBt rule (e.g., Tables I and II) with K=0 & M=0 can be appliedwhen the node decomposition level reaches 0 (or a last level).Otherwise, if the asymmetric bounding box flag is the second value, thefirst partition method can perform Octree decomposition (or Octreepartition).

According to the first partition method, the implicitQtBt rule shown inTable II with M=0 can be applied in order to skip sending unnecessaryoccupancy information along some dimensions, such as shown in Table III.

TABLE III Conditions to perform implicit QtBt partition at level 0 QTalong QT along QT along x-y axes x-z axes y-z axes Condition d_(z) = 0 <d_(x) = d_(y) d_(y) = 0 < d_(x) = d_(z) d_(x) = 0 < d_(y) = d_(z) BTalong BT along BT along x axis y axis z axis Condition d_(y) = 0 ≤ d_(z)< d_(x) d_(x) = 0 ≤ d_(z) < d_(y) d_(x) = 0 ≤ d_(y) < d_(z) d_(z) = 0 ≤d_(y) < d_(x) d_(z) = 0 ≤ d_(x) < d_(y) d_(y) = 0 ≤ d_(x) < d_(z)

In an embodiment, a second partition method (or explicit QtBt partition)can be provided to send explicit signaling of a split-decision. Theexplicit signaling can be provided as opposed to the use of a fixedimplicit rule in the current QtBt partition.

The second partition method can include following features: (1) anexplicit QtBt enabled flag (e.g., explicit_qtbt_enabled_flag) can beintroduced to enable/disable explicit split-decision signaling while theasymmetric bounding box flag from the first partition method can stillbe introduced. (2) When the explicit QtBt enabled flag is set to a valuesuch as 0 (or a second value), the second partition method falls back to(or can equal to) the first partition method that is described above.Thus, if the asymmetric bounding box flag (e.g.,asymmetric_bbox_enabled_flag) is a value such as 1 (or a first value),the implicitQtBt rule (e.g., Tables I and II) with K=0 & M=0 can beapplied when the node decomposition level reaches 0 (or a last level).If the asymmetric bounding box flag is the second value, the secondpartition method can perform Octree decomposition (or Octree partition).In an embodiment, when the asymmetric bounding box flag is not used, andthe explicit QtBt enabled flag is set to the second value (e.g., 0), thesecond partition method can apply Octree-decomposition (orOctree-partition) for all levels. (4) When the explicit QtBt enabledflag is set to the first value (e.g., 1), instead of always performingoctree-split until the level reaches 0 (or a last level) as mentioned inthe first partition method, a 3-bit signal can be sent in each of theoctree-levels to indicate whether to split along each of the x, y and zaxes. Thus, the 3-bit signal can indicate if a Bt partition, a Qtpartition, or a Ot partition can be applied in each of theoctree-levels. In some embodiments, the implicitQtBt rule (e.g., TablesI and II) in TMC 13 can be applied to determine the 3-bit signal in eachof the octree-levels.

It should be noted that as Ot/Qt/Bt partition is allowed in an arbitrarymanner along the way when the explicit QtBt enabled flag is set to thefirst value in the second partition method, the maximum possible totalnumber of splits can be three times a difference of maximum and minimumnode depths.

In an embodiment of the disclosure, a third partition method (orexplicit QtBt type-2 partition can be provided to send explicitsignaling of a split-decision. The explicit signaling can be provided asopposed to the use of a fixed implicit rule in the current QtBtpartition (e.g., Tables I and II). The third partition method caninclude the following features as compared to the current QtBtpartition: (1) an explicit QtBt enabled flag (e.g.,explicit_qtbt_enabled_flag) can replace the implicit QtBt enabled flag(e.g., implicit_qtbt_enabled_flag) in the QtBt partitions in TMC 13 toenable/disable explicit split-decision signaling. (2) An asymmetricbounding box flag (e.g., asymmetric_bbox_enabled_flag) can beadditionally signaled only when the explicit QtBt enabled flag is avalue such as 1 (or a first value) to enable/disable the use of anasymmetric bounding box. When the asymmetric bounding box flag is thefirst value, the asymmetric bounding box dimensions (i.e., sizes) alongx, y and z can further be signaled as opposed to the maximum of thethree. Accordingly, the asymmetric bounding box flag can be set to avalue such as 0 (e.g., a second value) for symmetric or almost symmetricbounding-box data and to the first value (e.g., 1) for highly asymmetricbounding-box data. (3) When the explicit QtBt enabled flag is set to thefirst value, a 3-bit signal can be sent in each of the octree-levels (oroctree partition levels) to indicate whether to split along each of thex, y and z axes. In an embodiment, the implicitQtBt rule (e.g., Tables Iand II) in TMC 13 can be applied to determine the 3-bit signal for eachof the octree-levels. In another embodiment, other splitting rules canbe applied to determine the 3-bit signal for each of the octree-levels.The other splitting rules can facilitate the coding of octree occupancyinformation and further take into account the characteristics of thedata (e.g., octree occupancy information) or an acquisition mechanism ofthe date. (4) When the explicit QtBt enabled flag is set to the secondvalue (e.g., 0), the third partition method can applyoctree-decomposition (octree-partition) for all levels.

It should be noted that as Ot/Qt/Bt partition is allowed in an arbitrarymanner along the way when the explicit QtBt enabled flag is set to thefirst value in the third partition method, the maximum possible totalnumber of splits can be three times a difference of maximum and minimumnode depths.

In an embodiment of the disclosure, a fourth partition method (orflexible QtBt partition) can be provided to provide more flexibility inthe use of QtBt partition by an additional signaling of the type of QtBtpartition either as explicit or implicit. The addition signaling can beprovided as opposed to the use of a fixed implicit rule in the currentQtBt partition (e.g., Tables I and II).

The fourth partition method can include: (1) a QtBt enabled flag (e.g.,qtbt_enabled_flag) can be applied to replace an implicit QtBt enabledflag in the QtBt partition in TMC 13 to indicate the use of QtBtpartition with more flexibility. (2) A QtBt type flag (e.g.,qtbt_type_flag) can be additionally signaled when the QtBt enabled flagis set to a value such as 1. (3) If the QtBt type flag is set to a valuesuch as 0, the current implicit QtBt scheme (e.g., Tables I and II) canbe applied. In addition, an asymmetric bounding box flag (e.g.,asymmetric_bbox_enabled_flag) can be additionally signaled toselectively enable/disable the use of an asymmetric bounding box. In anembodiment, when the asymmetric bound box flag is set to a value such as1, the asymmetric bounding box dimensions (i.e., sizes) along x, y and zcan be signaled as opposed to the maximum of the three. In anotherembodiment, when the asymmetric bound box flag is not signaled, theasymmetric bounding box can always be used.

The fourth partition method can also include: (4) If the QtBt type flagis a value such as 1, a 3-bit signal can be sent to each of theoctree-levels to indicate whether to split along each of the x, y and zaxes. In an embodiment, the implicitQtBt rule (e.g., Tables I and II) inTMC 13 can be applied to determine the 3-bit signal for each of theoctree-levels. In another embodiment, other splitting rules can beapplied to determine the 3-bit signal for each of the octree-levels. Theother splitting rules can facilitate the coding of octree occupancyinformation and further take into account the characteristics of thedata (e.g., octree occupancy information) or an acquisition mechanism ofthe date. In an embodiment, the asymmetric bound box flag can beadditionally signaled to selectively enable/disable the use ofasymmetric bounding box. When the asymmetric bound box flag is a valuesuch as 1, the asymmetric bounding box dimensions (i.e., sizes) along x,y and z can be signaled as opposed to the maximum of the three. Inanother embodiment, the asymmetric bound box flag may not be signaledand an asymmetric bounding box can always be used. (5) When a QtBtenabled flag is set to a value such as 0, the fourth partition methodcan apply octree-decomposition (or octree-partition) for all levels.

It should be noted that as Ot/Qt/Bt partition is allowed in an arbitrarymanner along the way when the explicit QtBt enabled flag is set to avalue such as 1 in the fourth partition method, the maximum possibletotal number of splits can be three times a difference of maximum andminimum node depths.

The above techniques can be implemented in a video encoder or decoderadapted for point cloud compression/decompression. The encoder/decodercan be implemented in hardware, software, or any combination thereof,and the software, if any, can be stored in on or more non-transitorycomputer readable media. For example, each of the methods (orembodiments), encoders, and decoders may be implemented by processingcircuitry (e.g., one or more processors or one or more integratedcircuits). In one example, the one or more processors execute a programthat is stored in a non-transitory computer-readable medium.

FIGS. 14 and 15 show flow charts outlining a process (1400) and aprocess (1500) according to embodiments of the disclosure. The processes(1400) and (1500) can be used during decoding processes for pointclouds. In various embodiments, the processes (1400) and (1500) can beexecuted by processing circuitry, such as the processing circuitry inthe terminal devices (110), the processing circuitry that performsfunctions of the encoder (203) and/or the decoder (201), the processingcircuitry that performs functions of the encoder (300), the decoder(400), the encoder (700), and/or the decoder (800), and the like. Insome embodiments, the processes (1400) and (1500) can be implemented insoftware instructions, thus when the processing circuitry executes thesoftware instructions, the processing circuitry performs the processes(1400) and (1500) respectively.

As shown in FIG. 14 , the process (1400) starts at (S1401) and proceedsto (S1410).

At (S1410), first signaling information can be received from a codedbitstream for a point cloud that includes a set of points in athree-dimensional (3D) space. The first signaling information canindicate partition information of the point cloud.

At (S1420), second signaling information can be determined based on thefirst signaling information indicating a first value. The secondsignaling information can be indicative of a partition mode of the setof points in the 3D space.

At (S1430), the partition mode of the set of points in the 3D space canbe determined based on the second signaling information. The process(1400) can then proceed to (S1440), where the point cloud can bereconstructed subsequently based on the partition mode.

In some embodiments, the partition mode can be determined to be apre-defined Quad-tree and Binary-tree (QtBt) partition based on thesecond signaling information being a second value.

In the process (1400), third signaling information can be received thatindicates the 3D space is an asymmetric cuboid. Dimensions of the 3Dspace that are signaled along x, y, and z directions can be determinedbased on the third signaling information being the first value.

In some embodiments, 3-bit signaling information can be determined foreach of a plurality of partition levels in the partition mode based onthe second signaling information being the first value. The 3-bitsignaling information for each of the plurality of partition levels canbe indicative of partition directions along x, y, and z directions forthe respective partition level in the partition mode.

In some embodiments, the 3-bit signaling information can be determinedbased on dimensions of the 3D space.

In the process (1400), the partition mode can be determined based on thefirst signaling information being a second value, where the partitionmode can include a respective octree-partition in each of a plurality ofpartition levels in the partition mode.

As shown in FIG. 15 , the process (1500) starts at (S1501) and proceedsto (S1510).

At (S1510), first signaling information can be received from a codedbitstream for a point cloud that includes a set of points in athree-dimensional (3D) space. The first signaling information can beindicative of partition information of the point cloud.

At (S1520), a partition mode of the set of points in the 3D space can bedetermined based on the first signaling information, where the partitionmode can include a plurality of partition levels.

At (S1530), the point cloud can subsequently be reconstructed based onthe partition mode.

In some embodiments, 3-bit signaling information for each of a pluralityof partition levels in the partition mode can be determined based on thefirst signaling information being a first value, where the 3-bitsignaling information for each of the plurality of partition levels canbe indicative of partition directions along x, y, and z directions forthe respective partition level in the partition mode.

In some embodiments, the 3-bit signaling information can be determinedbased on dimensions of the 3D space.

In some embodiments, the partition mode can be determined to include arespective octree-partition in each of the plurality of partition levelsin the partition mode based on the first signaling information being asecond value.

In the process (1500), second signaling information can further bereceived from the coded bitstream for the point cloud. The secondsignaling information can indicate the 3D space is an asymmetric cuboidwhen the second signaling information is a first value, and the 3D spaceis a symmetric cuboid when the second signaling information is a secondvalue.

In some embodiments, based on the first signal information indicatingthe second value and the second signal information indicating the firstvalue, the partition mode can be determined to include a respectiveoctree-partition in each of first partition levels in the plurality ofpartition levels of the partition mode. A partition type and a partitiondirection of a last partition level of the plurality of partition levelsof the partition mode can be determined according to a table as follows:

Partition type Qt along Qt along Qt along and direction x-y axes x-zaxes y-z axes Condition d_(z) = 0 < d_(x) = d_(y) d_(y) = 0 < d_(x) =d_(z) d_(x) = 0 < d_(y) = d_(z) Partition type Bt along Bt along Btalong and direction x axis y axis z axis Condition d_(y) = 0 ≤ d_(z) <d_(x) d_(x) = 0 ≤ d_(z) < d_(y) d_(x) = 0 ≤ d_(y) < d_(z) d_(z) = 0 ≤d_(y) < d_(x) d_(z) = 0 ≤ d_(x) < d_(y) d_(y) = 0 ≤ d_(x) < d_(z),wherein the d_(x), d_(y), and d_(z) are log 2 sizes of the 3D space inthe x, y, and z directions, respectively.

In the process (1500), second signaling information can be determinedbased on the first signaling information indicating a first value. Thesecond signaling information can indicate the 3D space is an asymmetriccuboid when the second signaling information indicates the first value,and the 3D space is a symmetric cuboid when the second signalinginformation indicates a second value. Further, dimensions of the 3Dspace that are signaled along x, y, and z directions can be determinedbased on the second signaling information indicating the first value.

As noted above, techniques described above can be implemented ascomputer software using computer-readable instructions and physicallystored in one or more computer-readable media. For example, FIG. 16shows a computer system (1800) suitable for implementing certainembodiments of the disclosed subject matter.

The computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code comprising instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by one or more computer central processingunits (CPUs), Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 16 for computer system (1800) are exemplaryin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementingembodiments of the present disclosure. Neither should the configurationof components be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary embodiment of a computer system (1800).

Computer system (1800) may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): keyboard (1801), mouse (1802), trackpad (1803), touchscreen (1810), data-glove (not shown), joystick (1805), microphone(1806), scanner (1807), camera (1808).

Computer system (1800) may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouch-screen (1810), data-glove (not shown), or joystick (1805), butthere can also be tactile feedback devices that do not serve as inputdevices), audio output devices (such as: speakers (1809), headphones(not depicted)), visual output devices (such as screens (1810) toinclude CRT screens, LCD screens, plasma screens, OLED screens, eachwith or without touch-screen input capability, each with or withouttactile feedback capability—some of which may be capable to output twodimensional visual output or more than three dimensional output throughmeans such as stereographic output; virtual-reality glasses (notdepicted), holographic displays and smoke tanks (not depicted)), andprinters (not depicted).

Computer system (1800) can also include human accessible storage devicesand their associated media such as optical media including CD/DVD ROM/RW(1820) with CD/DVD or the like media (1821), thumb-drive (1822),removable hard drive or solid state drive (1823), legacy magnetic mediasuch as tape and floppy disc (not depicted), specialized ROM/ASIC/PLDbased devices such as security dongles (not depicted), and the like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

Computer system (1800) can also include an interface to one or morecommunication networks. Networks can for example be wireless, wireline,optical. Networks can further be local, wide-area, metropolitan,vehicular and industrial, real-time, delay-tolerant, and so on. Examplesof networks include local area networks such as Ethernet, wireless LANs,cellular networks to include GSM, 3G, 4G, 5G, LTE and the like, TVwireline or wireless wide area digital networks to include cable TV,satellite TV, and terrestrial broadcast TV, vehicular and industrial toinclude CANBus, and so forth. Certain networks commonly require externalnetwork interface adapters that attached to certain general purpose dataports or peripheral buses (1849) (such as, for example USB ports of thecomputer system (1800)); others are commonly integrated into the core ofthe computer system (1800) by attachment to a system bus as describedbelow (for example Ethernet interface into a PC computer system orcellular network interface into a smartphone computer system). Using anyof these networks, computer system (1800) can communicate with otherentities. Such communication can be uni-directional, receive only (forexample, broadcast TV), uni-directional send-only (for example CANbus tocertain CANbus devices), or bi-directional, for example to othercomputer systems using local or wide area digital networks. Certainprotocols and protocol stacks can be used on each of those networks andnetwork interfaces as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces can be attached to a core (1840) of thecomputer system (1800).

The core (1840) can include one or more Central Processing Units (CPU)(1841), Graphics Processing Units (GPU) (1842), specialized programmableprocessing units in the form of Field Programmable Gate Areas (FPGA)(1843), hardware accelerators for certain tasks (1844), and so forth.These devices, along with Read-only memory (ROM) (1845), Random-accessmemory (1846), internal mass storage such as internal non-useraccessible hard drives, SSDs, and the like (1847), may be connectedthrough a system bus (1848). In some computer systems, the system bus(1848) can be accessible in the form of one or more physical plugs toenable extensions by additional CPUs, GPU, and the like. The peripheraldevices can be attached either directly to the core's system bus (1848),or through a peripheral bus (1849). Architectures for a peripheral businclude PCI, USB, and the like.

CPUs (1841), GPUs (1842), FPGAs (1843), and accelerators (1844) canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in ROM(1845) or RAM (1846). Transitional data can be also be stored in RAM(1846), whereas permanent data can be stored for example, in theinternal mass storage (1847). Fast storage and retrieve to any of thememory devices can be enabled through the use of cache memory, that canbe closely associated with one or more CPU (1841), GPU (1842), massstorage (1847), ROM (1845), RAM (1846), and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of the present disclosure, or they can be of the kind wellknown and available to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system havingarchitecture (1800), and specifically the core (1840) can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core (1840) that are of non-transitorynature, such as core-internal mass storage (1847) or ROM (1845). Thesoftware implementing various embodiments of the present disclosure canbe stored in such devices and executed by core (1840). Acomputer-readable medium can include one or more memory devices orchips, according to particular needs. The software can cause the core(1840) and specifically the processors therein (including CPU, GPU,FPGA, and the like) to execute particular processes or particular partsof particular processes described herein, including defining datastructures stored in RAM (1846) and modifying such data structuresaccording to the processes defined by the software. In addition or as analternative, the computer system can provide functionality as a resultof logic hardwired or otherwise embodied in a circuit (for example:accelerator (1844)), which can operate in place of or together withsoftware to execute particular processes or particular parts ofparticular processes described herein. Reference to software canencompass logic, and vice versa, where appropriate. Reference to acomputer-readable media can encompass a circuit (such as an integratedcircuit (IC)) storing software for execution, a circuit embodying logicfor execution, or both, where appropriate. The present disclosureencompasses any suitable combination of hardware and software.

While this disclosure has described several exemplary embodiments, thereare alterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods which, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

What is claimed is:
 1. A method of point cloud geometry decoding in apoint cloud decoder, comprising: receiving, by processing circuitry andfrom a coded bitstream for a point cloud that includes a set of pointsin a three-dimensional (3D) space, first signaling information beingindicative of partition information of the point cloud; receiving secondsignaling information that indicates whether the 3D space is anasymmetric cuboid; determining dimensions of the 3D space that aresignaled along x, y, and z directions based on the second signalinginformation indicating that the 3D space is the asymmetric cuboid; inresponse to the first signaling information indicating that the 3D spaceis the asymmetric cuboid, partitioning, by the processing circuitry, the3D space based on the determined dimensions of the 3D space and based onthe partition information indicated by the first signaling information;and reconstructing, by the processing circuitry, the point cloud basedon the partitioned 3D space.
 2. The method of claim 1, furthercomprising: in response to the first signaling information indicating asecond value, determining a partition mode to be a pre-defined Quad-treeand Binary-tree (QtBt) partition based on the determined dimensions ofthe 3D space.
 3. The method of claim 2, further comprising: in responseto the first signaling information indicating a first value,partitioning the 3D space using octree partitioning at all partitionlevels except a last partition level, and partitioning the lastpartition level using the pre-defined QtBt partition, based on thedetermined dimensions of the 3D space.
 4. The method of claim 1, furthercomprising: in response to the first signaling information indicating asecond value, receiving 3-bit signaling information for each of aplurality of partition levels, the 3-bit signaling information for eachof the plurality of partition levels being indicative of partitiondirections along x, y, and z directions for the respective partitionlevel.
 5. The method of claim 4, wherein the 3-bit signaling informationis determined based on the dimensions of the 3D space.
 6. The method ofclaim 1, further comprising: in response to the first signalinginformation indicating a first value, determining a partition mode asincluding a respective octree-partition in each of a plurality ofpartition levels in the partition mode.
 7. A method of point cloudgeometry decoding in a point cloud decoder, comprising: receiving, byprocessing circuitry and from a coded bitstream for a point cloud thatincludes a set of points in a three-dimensional (3D) space, firstsignaling information being indicative of whether explicitsplit-decision signaling is enabled or disabled; in response to thefirst signaling information indicating that the explicit split-decisionsignaling is enabled, receiving second signaling information thatindicates whether the 3D space is an asymmetric cuboid; and in responseto the second signaling information indicating that the 3D space is theasymmetric cuboid, determining dimensions of the 3D space; andpartitioning, by the processing circuitry, the 3D space based on thedetermined dimensions of the 3D space, the partitioning including aplurality of partition levels; and reconstructing, by the processingcircuitry, the point cloud based on the partitioned 3D space.
 8. Themethod of claim 7, further comprising: in response to the firstsignaling information indicating that the explicit split-decisionsignaling is enabled, receiving 3-bit signaling information for each ofthe plurality of partition levels, the 3-bit signaling information foreach of the plurality of partition levels being indicative of partitiondirections along x, y, and z directions for the respective partitionlevel.
 9. The method of claim 8, wherein the 3-bit signaling informationis determined based on the dimensions of the 3D space.
 10. The method ofclaim 7, further comprising: in response to the first signalinginformation indicating that the explicit split-decision signaling isdisabled, determining a partition mode that includes a respectiveoctree-partition in each of the plurality of partition levels as thepartition mode.
 11. The method of claim 7, further comprising: inresponse to the first signaling information indicating that the explicitsplit-decision signaling is enabled, determining a partition mode to bea pre-defined Quad-tree and Binary-tree (QtBt) partition.
 12. The methodof claim 11, wherein the pre-defined QtBt partition defines a partitiontype and a partition direction in each partition level in the pluralityof partition levels according to conditions as follows: Partition typeQt along Qt along Qt along and direction x-y axes x-z axes y-z axesCondition d_(z) = 0 < d_(x) = d_(y) d_(y) = 0 < d_(x) = d_(z) d_(x) = 0< d_(y) = d_(z) Partition type Bt along Bt along Bt along and directionx axis y axis z axis Condition d_(y) = 0 ≤ d_(z) < d_(x) d_(x) = 0 ≤d_(z) < d_(y) d_(x) = 0 ≤ d_(y) < d_(z) d_(z) = 0 ≤ d_(y) < d_(x) d_(z)= 0 ≤ d_(x) < d_(y) d_(y) = 0 ≤ d_(x) < d_(z),

wherein d_(x), d_(y), and d_(z) are log2 sizes of the 3D space in x, y,and z directions, respectively.
 13. The method according to claim 7,wherein the determining the dimensions of the 3D space comprisesreceiving third signaling information indicating asymmetric bounding boxdimensions in x, y, and z directions.
 14. An apparatus of processingpoint cloud data, comprising: processing circuitry configured to:receive first signaling information from a coded bitstream for a pointcloud that includes a set of points in a three-dimensional (3D) space,the first signaling information being indicative of whether explicitsplit-decision signaling is enabled or disabled; in response to thefirst signaling information indicating that the explicit split-decisionsignaling is enabled, receive second signaling information thatindicates whether the 3D space is an asymmetric cuboid; in response tothe second signaling information indicating that the 3D space is theasymmetric cuboid, determine dimensions of the 3D space; partition the3D space based on the determined dimensions of the 3D space, thepartitioning including a plurality of partition levels; and reconstructthe point cloud based on the partitioned 3D space.
 15. The apparatus ofclaim 14, wherein the processing circuitry is further configured to: inresponse to the first signaling information indicating that the explicitsplit-decision signaling is enabled, receive 3-bit signaling informationfor each of the plurality of partition levels, the 3-bit signalinginformation for each of the plurality of partition levels beingindicative of partition directions along x, y, and z directions for therespective partition level.
 16. The apparatus of claim 15, wherein the3-bit signaling information is determined based on the dimensions of the3D space.
 17. The apparatus of claim 14, wherein the processingcircuitry is further configured to: in response to the first signalinginformation indicating that the explicit split-decision signaling isdisabled, determine a partition mode that includes a respectiveoctree-partition in each of the plurality of partition levels as thepartition mode.
 18. The apparatus of claim 17, wherein the processingcircuitry is further configured to: in response to the first signalinginformation indicating that the explicit split-decision signaling isenabled, determine the partition mode to be a pre-defined Quad-tree andBinary-tree (QtBt) partition.
 19. The apparatus of claim 18, wherein thepre-defined QtBt partition defines a partition type and a partitiondirection in each partition level in the plurality of partition levelsaccording to conditions as follows: Partition type Qt along Qt along Qtalong and direction x-y axes x-z axes y-z axes Condition d_(z) = 0 <d_(x) = d_(y) d_(y) = 0 < d_(x) = d_(z) d_(x) = 0 < d_(y) = d_(z)Partition type Bt along Bt along Bt along and direction x axis y axis zaxis Condition d_(y) = 0 ≤ d_(z) < d_(x) d_(x) = 0 ≤ d_(z) < d_(y) d_(x)= 0 ≤ d_(y) < d_(z) d_(z) = 0 ≤ d_(y) < d_(x) d_(z) = 0 ≤ d_(x) < d_(y)d_(y) = 0 ≤ d_(x) < d_(z),

wherein d_(x), d_(y), and d_(z) are log2 sizes of the 3D space in x, y,and z directions, respectively.
 20. The apparatus according to claim 14,wherein the processing circuitry is further configured to determine thedimensions of the 3D space by receiving third signaling informationindicating asymmetric bounding box dimensions in x, y, and z directions.